Assessing RADARSAT Constellation Mission sea ice surface topography retrievals using data from ICESat-2
Bibliographic record
Abstract
Recent sea ice dynamics research and ice forecasts focus on the importance of sea ice topography and thickness. Sea ice topography is a critical component in sea ice drag forces, understanding sea ice motion and extent, heat transfer at the ocean-atmosphere interface, and safe ice-related decisions in marine navigation. The overarching objective of this study is to assess the inter-relationships of optical laser altimeter Ice, Cloud, and Land Elevation Satellite-2 (IS-2, 2019-Present) and the C-band frequency RADARSAT Constellation Mission (RCM, 2019-Present) synthetic aperture radar (SAR) to provide near-continual measurements of sea ice topography at the regional scale. For this study, a survey of first-year ice (FYI) and multi-year ice (MYI) in the McClintock Channel portion of the Canadian Arctic Archipelago (CAA) is completed during the winter and spring/summer seasons. RCM ScanSAR mode scenes are regionally co-located with the heights and calculated surface roughness for sea ice from Advanced Topographic Laser Altimeter System (ATLS 07) on IS-2. The IS-2 measured sea ice parameter data provide a vital cross-comparison of RCM measured backscatter variables. An object-based image analysis is used to link the IS-2 measured variables and RCM backscatter quantitatively. For data optimization, 12 bands from RCM are analyzed, including calibrated backscatter channels (HH and HV), their combinations (ratio, addition, subtraction, and multiplication), and a set of grey-level co-occurrence matrix (GLCM) based texture parameters derived from each backscatter channel, following the derivations provided in Scharien and Nasonova (2020). IS-2 ATL07 data are studied to measure sea ice surface elevation and roughness. Overall, strong positive linear relationships between backscatter and IS-2-derived surface roughness and elevation are found during the late winter (April and May) period, which supports the use of dual-polarization (HH and HV) RCM scenes and the combination of these channels (HH+HV) as complements to ATL07/IS-2 for understanding winter FYI and MYI ice topography. Generally, RCM derived backscatter is more strongly correlated to IS-2 derived variables than is RCM derived texture. At high incidence angles (>42o) and during late winter conditions, the IS-2 sea ice surface elevation from MYI strongly correlates (r ~ 0.74) with backscatter. However, the correlation between MYI surface height and RCM backscatter during winter is reduced at low and moderate incidence angles (≤42°). The relationship between RCM backscatter and FYI elevation shows a high correlation (r ~0.74 to 0.75) at low and moderate incidence angles. Considering melting conditions, relationships between IS-2 and RCM variables are much weaker compared to winter conditions, though much stronger later in the melting season (July) than earlier (June). The results of this study suggest that the following techniques should be used to map the elevation of sea ice during the winter using RCM: (1) HH-polarization backscatter (r ~ 0.74) and a low incidence angle (FYI only), and (2) HH+HV polarization (r ~ 0.64) and low incidence angle (MYI only); (3) HH+HV polarization (r ~ 0.74) and high incidence angle (MYI only); and (4) HH-HV polarization (FYI) or HV (MYI) (r ~ 0.75 and 0.72, respectively) and moderate incidence angle. Overall, HH+HV can be the best representative band for FYI topographic investigations using RCM image with low incidence angle during winter.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".